Dry labs are increasingly important tools in laboratory sciences, especially for early-stage research. But the term doesn’t mean that only teetotalers can work with them. Dry labs are designed in contrast to wet labs and there are no benches in sight. In fact, dry lab scientists may never pick up a pipette. Dry labs focus on applied or computational mathematical analyses instead of physical experiments, using computer-generated models and simulations in place of samples and reagents. More importantly, they are a key component of lab 4.0.

A dry lab can be a low-risk option for startups, allowing initial exploratory work to be done without investing in consumables, safety equipment, or laboratory instruments. In a dry lab, experimental failures can be easily addressed by rewriting a few lines of code. Failures involve no loss of materials or harm to research subjects.

That doesn’t mean that dry labs are simple, easy, or low cost. Configuring laboratory informatics to work with dry lab setups is just as complex as in a traditional wet lab. Dry lab workflows are designed for computational and theoretical studies, rather than physical samples and experiments, but they must be built for interdisciplinary collaboration, modeling and simulation, and complicated data analysis.

This blog post will look at how dry labs function and what types of research they are used for.

Working in a Dry Lab

Dry labs use data as the reactants, and actionable knowledge becomes the product. Dry labs address areas of research that differ from traditional wet labs. For example, bioinformatics or cheminformatics are staples in dry labs, where complex biological or chemical datasets are collected and analyzed. These applications are becoming indispensable tools throughout the drug development lifecycle, from early-stage research to later-stage development.

Bioinformatics and cheminformatics leverage computational tools and techniques that accelerate the research and development process, improve efficiency, and reduce costs. However, dry labs can be adopted wherever computational modeling is useful for developing and testing real-world phenomena, from climate science to fluid dynamics and materials science.

Computational modeling through cheminformatics or bioinformatics is facilitated by integrating your informatics systems and instruments to access greater quantities of data, as well as improving the quality of that data. These advantages lead to enhanced data management and analysis in the dry lab models. An informatics system also makes the transition from dry lab to wet lab easier, because such systems enforce standardized data formats and protocols, ensuring consistency and comparability of FAIR data across different experiments and researchers.

A LIMS,ELN, CDS, or platform provides a dry lab with the well-known benefits of efficient workflows, data-driven decision making, data integrity and regulatory compliance, and improved reporting. A centralized data management system also enhances interdisciplinary collaboration, allowing researchers in different fields of study to access the same data sets and approach solutions from their individual frames of reference. This collaboration may lead to more breakthroughs in less time.

Key Technologies in Dry Labs

Dry labs are centered around computational and analytical work. Therefore, their key technologies revolve around handling, analyzing, and modeling data. Some of the many technologies that support dry lab modeling and workflows include high-performance computing, bioinformatics or cheminformatics software and tolls, data analysis and visualization software, artificial intelligence and machine learning (AI and ML), data storage and management systems, and last (but not least) modeling and simulation software.

Data analysis and visualization, AI and ML, and data storage and management systems serve much the same functions for computational models and simulations in dry labs as they do with physical experimentation data in wet labs.

However, the massive datasets generated in dry labs require additional technologies that are purpose-built for vast quantities of data. These technologies include high-performance computing, without which many of the omics would be impossible; or bioinformatics and cheminformatics software and tools, which enable predictive modeling.

Ensuring your data is FAIR, standardizing data formats, and Integrating instruments and systems will allow your existing lab to quickly take advantage of dry lab technologies.

Future Dry Lab Trends

The rapid advancement of computational technologies such as quantum computing and the accompanying increases in available data will drive the spread of dry labs into many scientific disciplines as lab4.0 continues to advance. New fields of study will arise, such as the following:

• computational systems pharmacology—studying drug interactions in complex biological systems by combining pharmacokinetics, pharmacodynamics, and systems biology
• digital twins in personalized medicine—a virtual you for everyone, to simulate aging or disease progression and test treatment options
• predictive toxic genomics—predicting the toxicity of chemicals or drugs will reduce the need for animal testing in every industry that uses this process

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Is your organization interested in setting up a dry lab?

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